Brain–Computer Interfaces - Index of

212 D.M. Taylor and M.E. Stetner
Many useful BCI systems consist of a limited number of targets at fixed locations
(e.g. typing programs or icon selection menus for the severely paralyzed). Therefore,
these systems are particularly well suited to efficiently use intracortical signals; the
neural activity can be decoded into specific target locations directly without needing
the user to generate (or the BCI system to decipher) the detailed muscle activations
and joint configurations that would be needed to physically move one’s arm to these
same fixed targets.
Dr. Krishna Shenoy and colleagues have recently demonstrated this concept of
rapid efficient selection from a small fixed number of targets using intracortical
signals [6]. These researchers trained monkeys in a center-out reaching task that
included a delay and a “go” cue. The animals had to first hold their hand at a center
start location; a radial target would then light up. However, the animal was trained
to wait to move to the target until a “go” cue was given after some delay. These
researchers were able to fairly reliably predict the target to which the animal was
about to move using only the neural activity generated while the animal was still
waiting for the cue to move. The researchers then implemented a real-time neural
decoder that selected the target directly based on neural activity generated before the
animal started to move. Decoding of the full arm movement path was not needed to
predict and select the desired target.
In any BCI, there is usually a trade off between speed and accuracy. In the Shenoy
study, the longer the BCI system collected the monkey’s neural activity while the
animal was planning its move, the more accurately the decoder could predict and
select the desired target. However, longer neural collection times mean fewer target
selections can be made per minute. These researchers optimized this time over
which the BCI collected neural activity for predicting intended target. This optimization
resulted in a target selection system with the best performance reported in
the literature at that time – 6.5 bits of information per second 3 in their best monkey,
which translates to about 15 words per minute if used in a typing program.
Fifteen words per minute is an impressive theoretical typing rate for a BCI, but
it does not yet exceed the rate of even a mediocre typist using a standard keyboard.
So why hasn’t any research group been able to show direct brain control surpassing
the old manual way of doing things? Many labs are able to record from over 50
neural signals at a time. Based on the Georgopoulos study, 50+ neurons should
be enough to surpass normal motor performance in some of these target selection
tasks. The answer lies in the way in which the neural signals must be collected
for real-time BCI applications. In the Georgopoulos study, the animal’s head had a
chamber affixed over an opening in the skull. The chamber lid was opened up each
day and a new electrode temporarily inserted into the cortex and moved slowly down
until a good directionally-tuned neuron was recorded well above the background
firing activity of all the other cells. Under these conditions, one can very accurately
3 Bits per second is a universal measure of information transfer rate that allows one to compare
BCI performance between different studies and research groups as well as compare BCIs with
other assistive technologies.